Modeling three-dimensional (3D) faces for the purpose of synthesizing novel face images from a single two-dimensional (2D) image is one of the most difficult and challenging tasks in computer vision, partly because of large variations in human faces. Researchers have been developing technologies for 3D face modeling without relying on 3D sensors due to the demands in many real-world operating scenarios that require efficient, uncooperative, and cost-effective solutions. Research topics in this field include shape-from-shading, shape-from-stereo, structure-from-motion, and shape-from-texture. However, the quality of 3D face models obtained from these methods is often not satisfactory, and, more importantly, many of these approaches require multiple images. Thus reconstructing a 3D face from a single 2D face image is extremely challenging.
To achieve realistic 3D face modeling, it becomes necessary to use prior knowledge of a statistical 3D face model. However, these methods are known to be computationally expensive and may require manually annotated control points or camera calibration. More efficient 3D face modeling approaches are desired in numerous applications, which demand real-time computation and less user cooperation. Applications include automated face recognition in surveillance video, access control, entertainment, and online 3D gaming, among others.